#counter #most_common
Find Most Frequently repeated Items in
a Sequence

Transforming and reducing data

To calculate the sum of
squares, do the following:

nums = [1, 2, 3, 4, 5]
s = sum(x * x for x in nums)

instead of:
s = sum((x * x for x in nums)) or
s = sum([x * x for x in nums])

It introduces an extra step and creates an extra list or tuple.

Source: Python cookbook

#class #methods
Title: __repr__ with __str__


—When we call Class it is represented by __str__ method by default, if there is not __str__ method, __repr__() method will be called.
—The __repr__() method returns the code representation of an instance, The __str__() method converts the instance to a string

! A good example is provided above

source: "Python Cookbook:

MRO (method resolution order) list is simply a linear ordering of all the base classes

#memory_management
Title: String interning
To alleviate memory that can be quickly consumed by strings, Python implements string interning
— A string will be interned if it is a compile-time constant, is not the production of constant folding or is not longer than 20 characters, and consists exclusively of ASCII letters, digits, or underscores.
—Empty strings are interned.
Source

Title: Namespaces
A namespace is a system that has a unique name for each and every object in Python. An object might be a variable or a method.

There are 3 types of namespaces:
built-in namespaces
global namespaces
local namespaces

e.g
var1 = 5 # global namespace
def some_func():
var2 = 6 # local namespace

The built-in namespace is always available when Python is running. You can list all built-in namespaces with dir(__builtins__)

Source

Title: getters and setters in Python
Getters and Setters are used to ensure data encapsulation in OOP.
In python they are not the same as in other languages, because private variables are not hidden in python.
Getters and Setters are used to add validation for getting and setting values or to avoid direct access of a class field.

📌 get() and set() methods don't fully work as getters and setters. In this case, python has a special function property()

Sources: Python cookbook and geeksforgeeks

#memory_management
Title: Integer Caching in Python

The Python implementation front loads an array of integers between -5 to 256. Hence, variables referring to an integer within the range would be pointing to the same object that already exists in memory

Source

#memory_management
The Tricky Case with Operators 😃

Source

Assalomu alaykum
leetcode.com yoki binarysearch.com da masala ishlab yuradiganlar bo'lsa,
@dilshodbek_xojametov DIlshodbek botga ulangan gruppa yaratganlar, qiziquvchilar bo'lsa lichkalariga o'zingizni leetcode dagi yoki binarysearch.com dagi usernamizni jo'natsangiz gruppaga qo'shib qo'yishadi.

Guruh qoidasi:
- Har kuni kamida 1 ta misol yechish
- 2 kun ichida bitta ham misol yechmasa guruhdan kick bo'ladi

Guruhdan maqsad masala yechishni eslatib turish va ko'proq masala ishlashga qiziqtirish. O'zingizni reytingizni kuzatib yursangiz ham bo'ladi.
❗️RECOMMENDED

Today I have joined EPAM systems team as a software engineer, Sharing what I was learning in this channel helped me a lot during my interviews. Will do my best to continue sharing new knowledges 😃

Assalomu alaykum!
Oxirgi kunlarda juda ko'p so'ralgan savol bo'lgani uchun o'zimni intervyular haqida hozirgacha bo'lgan fikrlarimni va birozgina olgan tajribamni sizlar bilan ulashmoqchiman 😊

Demak boshladik ⏳

Mavzu: Texnik intervyularda Python dasturlash tilida backend dasturchilaridan so'rashlari mumkin bo'lgan mavzular to'plami yoki o'rganishimiz kerak bo'lgan mavzular to'plami desak ham to'g'riroq bo'ladi.


>> Data types in python
-difference between list and tuple etc;
-difference between mutable and immutable data types and how they are saved in the memory;
- sorting list/tuple/dictionaries;
>> id() function
>> Decorators
>> Iterators with Generators
-- difference between iterators and iterables
>> Python classes(self in class methods)
>> Context managers in Python
>> Threads/Processes/GIL
>> Garbage collectors
>> Memory management in Python
>> Reference counting in python
>> Questions related to Testing

➕ PROBLEM SOLVING

Qo'shimcha mavzular:

-- Big O notation
-- OOP/SOLID principles
-- CI/CD
-- Docker (images, containers)
-- Methodologies (Agile, Scrum)

--Database related questions:
> Relational database with non-relational databses
> Indexing
> queries with JOIN

➕ agar ishlatgan bo'lsangiz python frameworklari (django, FastAPi, Flask) va ularning ORMiga oid savollar ham bo'lishi mumkin.

va boshqalar )


📌 Shu yerda tavsiya: bu mavzularni faqat intervyu uchun o'rganmang, juda tez vaqt ichida yodingizdan ko'tarilib ketishi mumkin, aksincha real proyektlarda qo'llashga yoki kimgadur tushuntirib berishga harakat qiling shunda yodingizda ko'proq qoladi.

📌📌 Qo'shimcha tavsiya )
Bu mavzularni o'zini alohida izlab o'rgangandan ko'ra kitoblardan o'rganganimiz ancha foydaliroq va o'zimiz kutgandan ham ko'proq narsa olishimiz mumkin.

Hozircha men tavsiya qila oladigan resurslar:
Python_101 (by Michael Driscoll)
Python Coockbook (by David Beazley and Brian K. Jones)
Python tricks (Dan Badar)
Python basics: https://stepik.org/course/512/syllabus
Learning Python: Powerful Object-Oriented Programming
---------------------------------------------------------

Intervyular juda foydali deb o'ylayman, ayniqsa dasturchilar uchun. Sizni o'z sohangizda o'sishingizga yordam beradi va qayerlarda kamchiligingiz bor va nimalarni mustahkamlashingiz kerakligini yaqqol ko'rsatib beradi (xuddi peshonangizni devorga urgandek 😅).

Intervyularda olgan natijani to'g'ri qabul qiling, o'zingizga bo'lgan ishonchni so'ndirishiga qo'ymang yoki juda oson o'tgan bo'lsa o'zingizga ortiqcha baho berib miyangizni tormozlab qo'ymang.

Intervyulardan yiqilish yaxshi😃, Chunki keyingilarida yiqilishdan qo'rqmaysiz uje yiqilib ko'rgan bo'lasiz va yanayam yaxshiroq o'rganishingizga turtki bo'ladi.

Haqiqiy intervyudan oldin, do'stlaringizdan yoki tajribasi sizdan ko'proq insonlardan mock intervyular olib ko'rishlarini iltimos qiling, juda kotta yordam beradi bu ham. Hamda bitta joyga emas bir nechta joylarga apply qilib ko'rish kerak.

#call_stack

The call stack. This is the main structure of a running Python program. It has one item—a "frame"—for each currently active function call, with the bottom of the stack being the entry point of the program. Every function call pushes a new frame onto the call stack, and every time a function call returns, its frame is popped off.

Sourse1, source2

print() function returns None in python, which means whenever we call print() it references to None object in python memory.

e.g
my_var = print("something")

!!! Here my_var is None

Namespaces have different lifetimes, because they are often created at different points in time.

📌 The namespace containing the built-in names is created when the Python interpreter starts up, and is never deleted.

📌 The global namespace of a module is generated when the module is read in. Module namespaces normally last until the script ends, i.e. the interpreter quits.
📌 When a function is called, a local namespace is created for this function. This namespace is deleted either if the function ends, i.e. returns, or if the function raises an exception, which is not dealt with within the function.

source

Title: Scope

A scope (visibility of name) refers to a region of a program where a namespace can be directly accessed. Scopes are defined statically, but they are used dynamically.

e.g
var1 = 5 # global scope
def some_func(): # global scope
var2 = 6 # local scope

Python scope concept follows a rule known as the LEGB (Local, Enclosing, Global, and Built-in scopes).

When we call a name, Python starts searching it from local scopes and ends in built-is scopes. If it cannot find, you get a NameError.

#testing
Types of testing. (short notes)

Manual testing: testing is done without using any tools
➖Time consuming
➖Boring
➖Repetitive
➕ Lower cost in the short term
➕ Adaptable
➕ Flexible
➕ Easy to identify defects that automation tools may miss

Automated testing: testing is done by using a set of automated tools.

➖ Those tools can have limitations
➖ Can be expensive for an organization
➖Heavy reliance on tools

➕ Tests can be executed in parallel
➕Repeatable
➕ Quick and creative

Functional testing - tests whether or not the system is working properly. It can include both functional and automated testing.

Non-functional testing: Tests how well the system meets the requirements (performance, usability, reliability, etc.)